Efficient Convex Optimization of Reentry Trajectory via the Chebyshev Pseudospectral Method

A novel sequential convex (SCvx) optimization scheme via the Chebyshev pseudospectral method is proposed for efficiently solving the hypersonic reentry trajectory optimization problem which is highly constrained by heat flux, dynamic pressure, normal load, and multiple no-fly zones. The Chebyshev-Ga...

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Bibliographic Details
Published inInternational Journal of Aerospace Engineering Vol. 2019; no. 2019; pp. 1 - 9
Main Authors Yu, Chun-Mei, Yang, Ye, Zhao, Dang-Jun
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2019
Hindawi
John Wiley & Sons, Inc
Hindawi Limited
Wiley
Subjects
Aerospace engineering
Algorithms
Analysis
Chebyshev approximation
Computational geometry
Convex analysis
Convexity
Dynamic pressure
Embedded systems
Heat flux
Hypersonic reentry
Iterative methods
Mathematical programming
Methods
Nonlinear programming
Optimization algorithms
Reentry trajectories
Sea level
Spectral methods
Trajectory optimization
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Summary:A novel sequential convex (SCvx) optimization scheme via the Chebyshev pseudospectral method is proposed for efficiently solving the hypersonic reentry trajectory optimization problem which is highly constrained by heat flux, dynamic pressure, normal load, and multiple no-fly zones. The Chebyshev-Gauss Legend (CGL) node points are used to transcribe the original dynamic constraint into algebraic equality constraint; therefore, a nonlinear programming (NLP) problem is concave and time-consuming to be solved. The iterative linearization and convexification techniques are proposed to convert the concave constraints into convex constraints; therefore, a sequential convex programming problem can be efficiently solved by convex algorithms. Numerical results and a comparison study reveal that the proposed method is efficient and effective to solve the problem of reentry trajectory optimization with multiple constraints.
ISSN:1687-5966
1687-5974
DOI:10.1155/2019/1414279